How To Get Meaningful Results From Your Data With Azure Machine Learning?

Companies that have already built their data warehouses, now have the perfect momentum and a good technical base to make their data more valuable and better support their decisions. After the Business Intelligence boom, it’s time for more advanced and compounded solutions, including machine learning. And with tools such as Azure Machine Learning, you can make smarter, more precise and timely decisions.

There are in fact a few crucial fields of business operations that this mysterious analytical concept has changed. Today, it delivers huge value in diverse applications, such as demand or sales forecasting, failure and anomaly detection, online recommendations, advertisement targeting, but also in e.g. cognitive science.

And by embedding Machine Learning into their enterprise systems, various organizations can improve customer experience, reduce the risk of systematic failures, grow revenue and make significant cost savings.

Time-consuming and expensive systems…

However, building Machine Learning systems (on your own) is slow, time-consuming and error-prone. In such circumstances, lack of specific background and resources may cause a chronic frustration and will make you tear your hear out until you get bald.

Building a system based on Machine Learning algorithms requires deep expertise in statistics, econometrics, and artificial intelligence fields. Commercial Machine Learning systems are very expensive to deploy and maintain.

And that’s actually how and why the idea of Azure Machine Learning by Microsoft was born.

We made use of it and a while ago started to adopt a bunch of new BI skills into our competence map. Wanna know how it might affect your business?

Watch this:

Well, Microsoft created this service to allow you to build your own ML flows that cover cleansing the data, edit metadata, process, feature engineering and train your machine learning models. So why not use it and make a profit?!

Azure Machine Learning – no software, no hardware…

When I started my journey with Azure Machine Learning, I was hugely surprised since I didn’t need any software to install, no hardware to manage and no development environments to grapple with.

Azure Machine Learning is a comfortable environment to work with because you can log on to Azure and start developing Machine Learning models from scratch – in any location and on any device (based on an easy to use drag, drop and connect paradigm).

The service offers a wide range of data sources that you can connect, which is what really matters in data-driven/ big data word. Machine learning from Microsoft Azure supports connection directly with e.g.: Hive Query, Azure Blob Storage, Azure SQL and on-premises data sources (preview feature).

As everything, Azure Machine Learning has its limitations, which I’ve also discovered quickly. What are they?

The ability to create your models in R but not in Python… You can use Python only for data processing via Python script module.

Implementing Machine Learning model without an Internet connection is not possible. Depending on your data sensitivity, that may be a deal-breaker because all the algorithms, data, and results are in the cloud.

10GB storage data limitation to train your model (free pricing tier).

Azure Machine Learning – in comparison to its competitors – has a largecollection of the best-of-breed algorithms developed by Microsoft Research to solve regression, clustering and predictive scenarios.

You can also extend your experiment with custom algorithms in R using over 350 open source supported R packages.

Why does it matter and why now?

Data science offers organizations a real opportunity (with the right tool like Azure Machine Learning) to make smarter, more precise and timely decisions. They are no longer based on guesses or intuition, but on all the data they collect.

I mean, really,ALL OF IT (internal or external sources included, like weather conditions, social media updates, customer demographic and spatial data).

Wondering what specific data you can use in your predictions? Or how to set things up without messing around and making your analytical team’s life harder?

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Hello!

Hey, my name is Dawid and at Predica I am the one to play a "Data Philosopher" role. My main duties are all around BI and Data Analytics projects. What's it all about? Designing and implementing advanced analytics solutions including machine learning algorithms, multidimensional/tabular data models OLAP, designing, developing and implementing data warehouses or databases, as well as building self-service reporting platforms and analytical dashboards (while coding in T-SQL, C#, R, DAX, MDX). Lots of incredible stuff to share with the world!